Positions Available

Multiple research positions are available in the Triplett lab at UCLA focused on statistical machine learning methods for neuroscience and mechanistic models of neural computation and cognition. 

Example project areas

Project area 1: Statistical inference methods for mapping neural circuit connectivity and plasticity.

Project area 2: Computational models of cognition, especially for causal structure learning or causal representation learning; testing resulting theories in human intracranial recordings via collaborations.

Project area 3: AI-guided closed-loop stimulation, Bayesian experimental design algorithms, and modelling stimulation-evoked animal behavior.

Project area 4: Causality-based techniques for analyzing AI systems and/or biological neural circuits.

Other areas of interest: machine learning for neural encoding and decoding; brain-computer interfacing; connectomics; latent factor analysis, dimensionality reduction, and neural dynamics; internal state estimation; analysis of large-scale electrophysiological and imaging datasets; causality; Bayesian statistics.

Candidates should have strong computational skills and a background in a quantitative discipline (computer science, statistics, physics, electrical engineering, mathematics, etc). A background in neuroscience is not required.

For prospective PhD students: Students must first be admitted to a relevant graduate program at UCLA, such as:

• Neuroscience Interdepartmental Program (IDP)

• Biomathematics Program (in the Department of Computational Medicine)

• Computer Science

• Statistics & Data Science.

Interested students should contact Marcus with a CV, brief description of research interests, and what program they are enrolled in (or applying to).

For prospective postdocs: Please email Marcus directly at [email protected] with a CV and description of past research and future interests.

The UCLA campus at night